Introduction: Parasitic diseases affecting humans continue to be the leading cause of morbidity as well as mortality, particularly in tropical and subtropical countries. With the rise of multi-drug resistant forms of many diseases, it has become increasingly important to develop new strategies to identify alternative drug targets. One such strategy for analysis of protein sequences of pathogen that can provide useful insight and markers for drug target identification is the Subtractive Proteomics Approach (SPA). In this dataset between the host and pathogen proteome is subtracted and analysed which provides information pertaining to a set of proteins that are likely to be essential to the pathogen but absent in the host. The subtractive Proteomics Approach (SPA) is one of the most efficient methods which includes the use of various precise software databases. Aim: SPA analysis was to predict putative drug targets in parasitic species Leishmania major, Plasmodium falciparum, Trypanosoma brucei and Trypanosoma cruzi. Methods: Common proteins of these species were predicted using Transporter Database 2.0. Common protein was analyzed by BlastP tool that predicted proteins that are non-homologous to homo sapiens. Database of Essential Genes (DEG) database was used to predict essential proteins for parasites and results were subjected to CELLO tool to identify the subcellular localization of important protein targets. Prosite database identified important protein families and patterns which were present in drug targets. Results: Drug targets identified in parasitic species Leishmania major, Plasmodium falciparum, Trypanosoma brucei and Trypanosoma cruzi are mitochondrial Carrier family, Major Facilitator Superfamily, Sulfate Permease Family, P-type ATPase Superfamily and Major Intrinsic Protein Family respectively. Conclusion: Screening of drug targets using SPA approach has identified major drug targets in parasitic species and which are not present in human and can be investigated further by computational drug designing and systems biology approach.